Stationary stochastic processes for scientists and engineers
 Responsibility
 Georg Lindgren, Holger Rootzén, Maria Sandsten.
 Language
 English.
 Publication
 Boca Raton CRC Press, Taylor & Francis Group, an informa business, [2014]
 Copyright notice
 ©2014
 Physical description
 xv, 314 pages : illustrations (black and white) ; 25 cm
Access
Available online
Math & Statistics Library
Stacks
Call number  Status 

QA274.3 .L565 2014  Unknown 
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Creators/Contributors
 Author/Creator
 Lindgren, Georg, 1940 author.
 Contributor
 Rootzén, Holger, author.
 Sandsten, Maria, author.
Contents/Summary
 Bibliography
 Includes bibliographical references (pages 299303) and index.
 Contents

 Stochastic Processes Some stochastic models Definition of a stochastic process Distribution functions Stationary Processes Introduction Moment functions Stationary processes Random phase and amplitude Estimation of mean value and covariance function Stationary processes and the nonstationary reality Monte Carlo simulation from covariance function The Poisson Process and Its Relatives Introduction The Poisson process Stationary independent increments The covariance intensity function Spatial Poisson process Inhomogeneous Poisson process Monte Carlo simulation of Poisson processes Spectral Representations Introduction Spectrum in continuous time Spectrum in discrete time Sampling and the aliasing effect A few more remarks and difficulties Monte Carlo simulation from spectrum Gaussian Processes Introduction Gaussian processes The Wiener process Relatives of the Gaussian process The Levy process and shot noise process Simulation of Gaussian process from spectrum Linear FiltersGeneral Theory Introduction Linear systems and linear filters Continuity, differentiation, integration White noise in continuous time Crosscovariance and crossspectrum AR, MA, and ARMA Models Introduction Autoregression and moving average Estimation of AR parameters Prediction in AR and ARMA models A simple nonlinear modelthe GARCH process Monte Carlo simulation of ARMA processes Linear FiltersApplications Introduction Differential equations with random input The envelope Matched filter Wiener filter Kalman filter An example from structural dynamics Monte Carlo simulation in continuous time Frequency Analysis and Spectral Estimation Introduction The periodogram The discrete Fourier transform and the FFT Bias reductiondata windowing Reduction of variance Appendix A: Some Probability and Statistics Appendix B: Delta Functions and Stieltjes Integrals Appendix C: Kolmogorov's Existence Theorem Appendix D: Covariance/Spectral Density Pairs Appendix E: A Historical Background References Index Exercises appear at the end of each chapter.
 (source: Nielsen Book Data)
 Publisher's Summary
 Stochastic processes are indispensable tools for development and research in signal and image processing, automatic control, oceanography, structural reliability, environmetrics, climatology, econometrics, and many other areas of science and engineering. Suitable for a onesemester course, Stationary Stochastic Processes for Scientists and Engineers teaches students how to use these processes efficiently. Carefully balancing mathematical rigor and ease of exposition, the book provides students with a sufficient understanding of the theory and a practical appreciation of how it is used in reallife situations. Special emphasis is on the interpretation of various statistical models and concepts as well as the types of questions statistical analysis can answer. The text first introduces numerous examples from signal processing, economics, and general natural sciences and technology. It then covers the estimation of mean value and covariance functions, properties of stationary Poisson processes, Fourier analysis of the covariance function (spectral analysis), and the Gaussian distribution. The book also focuses on inputoutput relations in linear filters, describes discretetime autoregressive and moving average processes, and explains how to solve linear stochastic differential equations. It concludes with frequency analysis and estimation of spectral densities. With a focus on model building and interpreting the statistical concepts, this classroomtested book conveys a broad understanding of the mechanisms that generate stationary stochastic processes. By combining theory and applications, the text gives students a wellrounded introduction to these processes. To enable handson practice, MATLAB(R) code is available online.
(source: Nielsen Book Data)
Subjects
Bibliographic information
 Publication date
 2014
 Copyright date
 2014
 Note
 "A Chapman & Hall book."
 ISBN
 9781466586185 (hbk)
 1466586184 (hbk)